Light Detection and Ranging (LiDAR) remote sensing has demonstrated potential in measuring forest biomass. We assessed the ability of LiDAR to accurately estimate forest total above ground biomass (TAGB) on an individual stem basis in a conifer forest in the US Pacific Northwest region using three different computer software programs and compared results to field measurements. Software programs included FUSION, TreeVaW, and watershed segmentation. To assess the accuracy of LiDAR TAGB estimation, stem counts and heights were analyzed. Differences between actual tree locations and LiDAR-derived tree locations using FUSION, TreeVaW, and watershed segmentation were 2.05 m (SD 1.67), 2.19 m (SD 1.83), and 2.31 m (SD 1.94), respectively, in forested plots. Tree height differences from field measured heights for FUSION, TreeVaW, and watershed segmentation were −0.09 m (SD 2.43), 0.28 m (SD 1.86), and 0.22 m (2.45) in forested plots; and 0.56 m (SD 1.07 m), 0.28 m (SD 1.69 m), and 1.17 m (SD 0.68 m), respectively, in a plot containing young conifers. The TAGB comparisons included feature totals per plot, mean biomass per feature by plot, and total biomass by plot for each extraction method. Overall, LiDAR TAGB estimations resulted in FUSION and TreeVaW underestimating by 25 and 31% respectively, and watershed segmentation overestimating by approximately 10%. LiDAR TAGB underestimation occurred in 66% and overestimation occurred in 34% of the plot comparisons.

The human visualization system is not optimally suited for fire detection. Smoke occlusion heavily limits flame visibility and low flames can be difficult to see. Thermal infrared (TIR) sensors mitigate these effects but come at high costs (>$3000) that limit use. Digital cameras modified to record near-infrared (NIR) are potential alternatives and are much more affordable (<$500). We examined the effectiveness of a converted NIR camera for fire detection. Eleven burning slash piles were simultaneously imaged with both a NIR and a camera-sensitive visible or red, green, and blue (RGB) light to compare performance. Quantitative differences in image fire-to-background contrast and flame size were compared between the paired NIR and RGB images. Quantitative analysis was facilitated by Maximum Likelihood Classifier in ENVI. Differences between contrast ratios and flame sizes were assessed for statistical significance by randomization test. Results showed statistically significant (p < 0.01) increases in both contrast ratio and flame size in all NIR images. The low-cost alternative offered by this technology relative to proven thermal sensors is attractive and immediately accessible. NIR sensors will never be as effective as TIR, but at a fraction of the cost, these cameras can fill a significant void in fire line situational awareness.

Airborne Light Detection and Ranging (LiDAR) has become a popular remote sensing technology to create digital terrain models and provide forest inventory information. However, little research has been done to investigate the accuracy of using scanning airborne LiDAR to perform road geomatics tasks common to forest engineering. We used airborne LiDAR to estimate existing forest road characteristics in support of a road assessment under four different canopy conditions. In estimating existing road centerlines, LiDAR data had a vertical root mean squared error (RMSE) of 0.28 m and a horizontal RMSE of 1.21 m. Road grades were estimated to within 1% slope of the value sampled in the field and horizontal curve radii were estimated with an average absolute error of 3.17 m. The results suggest that airborne LiDAR is an acceptable data source to estimate forest road centerlines and grades, but some caution should be used in estimating horizontal curve radii, particularly on sharp curves.

Detect and Avoid (DAA) systems are complex communication and locational technologies comprising multiple independent components. DAA technologies support communications between ground-based and space-based operations with aircraft. Both manned and unmanned aircraft systems (UAS) rely on DAA communication and location technologies for safe flight operations. We examined the occurrence and duration of communication losses between radar and automatic dependent surveillance–broadcast (ADS-B) systems with aircraft operating in proximate airspace using data collected during actual flight operations. Our objectives were to identify the number and duration of communication losses for both radar and ADS-B systems that occurred within a discrete time period. We also investigated whether other unique communication behavior and anomalies were occurring, such as reported elevation deviations. We found that loss of communication with both radar and ADS-B systems does occur, with variation in the length of communication losses. We also discovered that other unexpected behaviors were occurring with communications. Although our data were gathered from manned aircraft, there are also implications for UAS that are operating within active airspaces. We are unaware of any previously published work on occurrence and duration of communication losses between radar and ADS-B systems.

Wildfires burn several million hectares in the United States annually. Time is critical in gathering information from burned landscapes for post-fire recovery planning. A technology to obtain spatial vegetation information across landscapes is Light Detecting and Ranging (LiDAR). We compared tree positional and height measurements, primarily from Douglas-fir (Pseudotsuga menziesii) and ponderosa pine (Pinus ponderosa), between field-based and LiDAR-derived measurements at three south-western Oregon (USA) sites. The sites represented a range of tree mortality from minimal to extensive. Our primary objective was to determine whether significant differences existed between field and LiDAR tree measurements in burned landscapes. Secondary objectives were to examine whether LiDAR pulse intensities in burned landscapes could differentiate coniferous from deciduous trees, discern fire-killed from live trees, and whether other tree measurement parameters were related to pulse intensities. No significant differences were detected between field-based and LiDAR-derived horizontal positions. Tree height differences between field-based and LiDAR measurements were significant at one site likely owing to dense canopy and measurement biases. Mean and maximum LiDAR intensities were significantly different between live and dead (fire-killed) trees in two of three sites. Additionally, crown diameter and tree sweep were significant in explaining variation in maximum LiDAR intensities at all sites.